Paper: Topic Segmentation with a Structured Topic Model

ACL ID N13-1019
Title Topic Segmentation with a Structured Topic Model
Venue Annual Conference of the North American Chapter of the Association for Computational Linguistics
Session Main Conference
Year 2013

We present a new hierarchical Bayesian model for unsupervised topic segmentation. This new model integrates a point-wise boundary sam- pling algorithm used in Bayesian segmenta- tion into a structured topic model that can cap- ture a simple hierarchical topic structure latent in documents. We develop an MCMC infer- ence algorithm to split/merge segment(s). Ex- perimental results show that our model out- performs previous unsupervised segmentation methods using only lexical information on Choi?s datasets and two meeting transcripts and has performance comparable to those pre- vious methods on two written datasets.